code: def __init__(self, model_folder, max_length=256, lowercase=True):
      # 1. Create tokenizer
      self.max_length = max_length
      vocab_file = os.path.join(model_folder, "vocab.txt")
      self.tokenizer = FullTokenizer(
          vocab_file, do_lower_case=lowercase
      )
  
      # 2. Read Config
      config_file = os.path.join(model_folder, "bert_config.json")
      self.config = BertConfig.from_json_file(config_file)
  
      # 3. Create Model
      self.session = tf.Session()
      self.token_ids_op = tf.placeholder(
          tf.int32, shape=(None, max_length), name="token_ids"
      )
      self.model = BertModel(
          config=self.config,
          is_training=False,
          input_ids=self.token_ids_op,
          use_one_hot_embeddings=False,
      )
  
      # 4. Restore Trained Model
      self.saver = tf.train.Saver()
      ckpt_file = os.path.join(model_folder, "bert_model.ckpt")
      self.saver.restore(self.session, ckpt_file)
  
      hidden_layers = self.config.num_hidden_layers
      self.embeddings_op = tf.get_default_graph().get_tensor_by_name(
          "bert/encoder/Reshape_{}:0".format(hidden_layers + 1)
      )
  
imports: import shutil
  import numpy
  import plyvel
  import os.path
  import sys
  from bert.modeling import *
  from bert.tokenization import *
  import json
  import os.path
  import numpy
label_desc: Use image node_id 1 to create a new BertEmbeddingsResolver object  with example: obj = BertEmbeddingsResolver(model_folder, max_length, lowercase)